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Record W2985710792

Impacts of Industrial Wind Turbine Noise on Sleep Quality: Results From a Field Study of Rural Residents in Ontario, Canada.

2016· article· en· W2985710792 on OpenAlexaboutno aff
James Lane, Philip Bigelow, Shannon E. Majowicz, R Stephen McColl

Bibliographic record

VenuePubMed · 2016
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsnot available
Fundersnot available
KeywordsActigraphyBedroomSleep (system call)Wind powerNoise (video)Sample (material)Environmental scienceTurbineEnvironmental healthQuality (philosophy)PsychologyStatisticsMedicineComputer scienceEngineeringMathematicsCircadian rhythmElectrical engineeringArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

The objectives of this study were to determine whether grid-connected industrial wind turbines (IWTs) are a risk factor for poor sleep quality, and if IWT noise is associated with sleep parameters in rural Ontarians. A daily sleep diary and actigraphy-derived measures of sleep were obtained from 12 participants from an IWT community and 10 participants from a comparison community with no wind power installations. The equivalent and maximum sound pressure levels within the bedroom were also assessed. No statistically significant differences were observed between IWT residents and non-IWT residents for any of the parameters measured in this study. Actigraphy and sleep diaries are feasible tools to understand the impact of IWTs on the quality of sleep for nearby residents. Further studies with larger sample sizes should be conducted to determine whether the lack of statistical significance observed here is a result of sample size, or reflects a true lack of association.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.078
GPT teacher head0.339
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations16
Published2016
Admission routes1
Has abstractyes

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